GitHub - JuliaML/LIBSVM.jl: LIBSVM bindings for Julia (original) (raw)

LIBSVM.jl

This is a Julia interface forLIBSVM and for the linear SVM model provided byLIBLINEAR.

Features:

• Supports all LIBSVM models: classification C-SVC, nu-SVC, regression: epsilon-SVR, nu-SVR and distribution estimation: one-class SVM
• Model objects are represented by Julia type SVM which gives you easy access to model features and can be saved e.g. as JLD file
• Supports ScikitLearn.jl API

Usage

LIBSVM API

This provides a lower level API similar to LIBSVM C-interface. See ?svmtrainfor options.

using LIBSVM using RDatasets using Printf using Statistics

Load Fisher's classic iris data

iris = dataset("datasets", "iris")

First four dimension of input data is features

X = Matrix(iris[:, 1:4])'

LIBSVM handles multi-class data automatically using a one-against-one strategy

y = iris.Species

Split the dataset into training set and testing set

Xtrain = X[:, 1:2:end] Xtest = X[:, 2:2:end] ytrain = y[1:2:end] ytest = y[2:2:end]

Train SVM on half of the data using default parameters. See documentation

of svmtrain for options

model = svmtrain(Xtrain, ytrain)

Test model on the other half of the data.

ŷ, decision_values = svmpredict(model, Xtest);

Compute accuracy

@printf "Accuracy: %.2f%%\n" mean(ŷ .== ytest) * 100

Precomputed kernel

It is possible to use different kernels than those that are provided. In such a case, it is required to provide a matrix filled with precomputed kernel values.

For training, a symmetric matrix is expected:

K = [k(x_1, x_1)  k(x_1, x_2)  ...  k(x_1, x_l);
     k(x_2, x_1)
         ...                            ...
     k(x_l, x_1)        ...         k(x_l, x_l)]

where x_i is i-th training instance and l is the number of training instances.

To predict n instances, a matrix of shape (l, n) is expected:

KK = [k(x_1, t_1)  k(x_1, t_2)  ...  k(x_1, t_n);
      k(x_2, t_1)
          ...                            ...
      k(x_l, t_1)        ...         k(x_l, t_n)]

where t_i is i-th instance to be predicted.

Example

Training data

X = [-2 -1 -1 1 1 2; -1 -1 -2 1 2 1] y = [1, 1, 1, 2, 2, 2]

Testing data

T = [-1 2 3; -1 2 2]

Precomputed matrix for training (corresponds to linear kernel)

K = X' * X

model = svmtrain(K, y, kernel=Kernel.Precomputed)

Precomputed matrix for prediction

KK = X' * T

ỹ, _ = svmpredict(model, KK)

ScikitLearn API

You can alternatively use ScikitLearn.jl API with same options as svmtrain:

using LIBSVM using RDatasets

Classification C-SVM

iris = dataset("datasets", "iris") X = Matrix(iris[:, 1:4]) y = iris.Species

Xtrain = X[1:2:end, :] Xtest = X[2:2:end, :] ytrain = y[1:2:end] ytest = y[2:2:end]

model = fit!(SVC(), Xtrain, ytrain) ŷ = predict(model, Xtest)

Epsilon-Regression

whiteside = RDatasets.dataset("MASS", "whiteside") X = Matrix(whiteside[:, 3:3]) # the Gas column y = whiteside.Temp

model = fit!(EpsilonSVR(cost = 10., gamma = 1.), X, y) ŷ = predict(model, X)

MLJ API

The MLJ interface to LIBSVM.jl consists of the following models:

• classification: LinearSVC, SVC, NuSVC
• regression: EpsilonSVR, NuSVR
• outlier detection: OneClassSVM

Each model has a detailed document string, which includes examples of usage. Document strings can be accessed from MLJ without loadingLIBSVM.jl (or its MLJ interface) as shown in the following example:

using MLJ # or MLJModels doc("NuSVC", pkg="LIBSVM")

This assumes the version of MLJModels loaded is 0.15.5 or higher.

Credits

The LIBSVM.jl library is currently developed and maintained by Matti Pastell. It was originally developed by Simon Kornblith.

LIBSVM by Chih-Chung Chang and Chih-Jen Lin